COMPARING DSPM SOLUTIONS
Evaluate DSPM solutions based on the capabilities that matter most, including data discovery, AI classification, exposure risk detection, AI data security, direct and delegated remediation, and integration with data loss prevention (DLP) across SaaS, cloud, and on-premises environments.
Overview
Proofpoint Data Security Posture Management (DSPM) discovers and classifies sensitive data across software as a service (SaaS), cloud, and on-premises environments. It helps security teams find and fix data exposure and over-permissioned access. Findings feed Proofpoint Enterprise DLP, Insider Threat Management, and Data Security for AI for policy enforcement.
DSPM EVALUATION CRITERIA
DSPM (data security posture management) shows where sensitive data lives, who can access it, and how exposed it is. Effective DSPM solutions connect discovery and classification to access governance and prevention. Use this framework to compare DSPM vendors on the criteria that matter most to enterprise teams.
| Evaluation area | Why it matters | What to look for |
|---|---|---|
| Data discovery and coverage | Organizations cannot protect data they cannot see, and sensitive data rarely stays in one environment | Discovery of regulated and business-critical data across SaaS, cloud, Microsoft 365, databases, on-premises file stores, and AI-adjacent sources |
| Classification depth and accuracy | Traditional rule-based classification might miss business-specific data, leaving critical exposure gaps | Context-based classification of structured and unstructured data, not just patterns, metadata, predefined templates, or fixed taxonomies |
| Data exposure risk | Teams must focus on the riskiest data exposures first | Identification of risky sharing, public access, misconfigurations, and over-permissioned users, groups, service accounts, and AI tools; one view that correlates sensitivity, ownership, share level, lineage, and last access |
| Over-permissioned access | Excess access for human and machine identities increases data exposure and insider risk | A clear view of users, groups, service accounts, and AI tools with excessive data access |
| Direct and delegated remediation | Exposure findings must lead to action and measurable risk reduction | Support for bulk admin actions, delegated data-owner review, deadlines, reminders, closure tracking, and audit history |
| Posture to prevention | DSPM should directly support data loss prevention (DLP), data access governance, and compliance | Direct connection of findings to DLP, access governance, and compliance workflows |
| AI data security | AI increases enterprise data risk | Detection of sensitive data used in model-training and retrieval-augmented generation (RAG) workflows |
| Operational fit | Long-term success depends on deployment effort, integrations, and reporting | Deployment model, integrations, reporting, workflow ownership, and ongoing tuning |
Securing AI Data
Adopting AI copilots and generative AI (GenAI) apps makes sensitive data accessible in new ways across SaaS, cloud, and Microsoft 365 environments. A modern DSPM solution helps you understand what data AI can access, identify exposure, and reduce risk before AI-enabled workflows are deployed.
Data Security Readiness for AI
Review over-permissioned users, groups, and external sharing before you deploy AI assistants.
Govern AI training data
Detect sensitive data in AI workflows, including RAG pipelines, and model-training data.
Find obsolete data before AI uses it
Identify stale data that could be exposed to users or AI tools.
Connect AI security with data protection
Use DSPM findings to natively create DLP policies and govern access.
DSPM vendors differ in what they cover, how they prioritize risk, and how well they help teams move from findings to action. As you evaluate vendors, consider which solution best fits your data environment, risk model, remediation workflows, and broader data security program.
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How well does it cover third-party SaaS, cloud platforms, and databases outside Microsoft environments? |
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| The Proofpoint difference | Proofpoint DSPM extends visibility beyond Microsoft environments. Teams discover and govern sensitive data across SaaS, cloud, and on-premises sources. Findings connect to DLP, insider risk, and AI data security workflows. |
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How far does it extend beyond discovery and classification into remediation and policy enforcement? |
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| The Proofpoint difference | Proofpoint DSPM helps teams move from data visibility to enforcement by prioritizing sensitive data exposure, identifying over-permissioned access, and connecting findings with remediation, DLP, and insider risk workflows. |
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How well does it support security use cases such as risk prioritization, remediation, and DLP integration? |
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| The Proofpoint difference | Proofpoint DSPM takes a unified data security approach. Teams identify sensitive data and understand access risk. Insights then connect to enforcement across DLP, data access governance, and AI data security. |
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How well does it support hybrid environments, delegated remediation, and DLP integration? |
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| The Proofpoint difference | Proofpoint DSPM takes a unified data security approach. Teams identify sensitive data and understand access risk. Insights then connect to enforcement workflows across DLP, data access governance, and AI data security. |
Why Proofpoint DSPM
Proofpoint DSPM helps you reduce risk wherever people, applications, and AI interact with sensitive data. You can discover and classify sensitive data across SaaS, cloud, and on-premises environments, prioritize exposure based on access risk and data sensitivity, and act through delegated remediation, DLP policy integration, Insider Threat Management, and Data Security for AI. Instead of managing discovery, governance, prevention, and AI data security separately, you can connect posture findings with the controls that help prevent data loss.
Discover sensitive data at scale
Find and classify sensitive data across SaaS, PaaS, IaaS, M365, databases, and hybrid data stores.
Prioritize exposure risk
Identify risky access, misconfigurations, attack paths, and high-value data assets.
Reduce over-permissioned access
Use admin and content-owner workflows to support least-privilege access and remediation.
Connect DSPM with DLP
Use DSPM context to enrich DLP policies, investigations, and response workflows.
Secure AI data use
Identify sensitive data used by AI tools, RAG workflows, and model-training pipelines.
Support compliance workflows
Map policy violations, monitor data hygiene, and generate reporting for audit readiness.
DSPM PROOF OF CONCEPT
Use the following questions to evaluate whether a DSPM solution can support your data environment, workflows, and risk model—not just demonstrate discovery in a controlled environment.
| Question | What to listen for |
|---|---|
| Which environments are supported today? | Look for coverage across SaaS, Microsoft 365, cloud, databases, on-premises file stores, and AI-related data sources. |
| How does scanning work? | Confirm whether data is scanned in place or moved elsewhere, and what that means for security, lineage, and performance. |
| How does classification handle business-specific data? | Ask whether the solution can identify proprietary documents, source code, contracts, financial models, and other sensitive data beyond regulated data types. |
| How are exposure risks prioritized? | Look for correlation across data sensitivity, ownership, access, behavior, lineage, and AI usage—not just a list of findings. |
| What happens when risky access is found? | Confirm whether teams can take direct action, delegate reviews to data owners, and track remediation to closure. |
| How does DSPM connect to DLP? | Ask whether DSPM findings can inform DLP policies, investigations, and enforcement without manual translation. |
| How does the solution support AI adoption? | Confirm visibility into data that copilots, GenAI apps, AI training pipelines, service accounts, and AI agents can access. |
| What will our team need to operate long term? | Evaluate deployment effort, integrations, reporting, workflow ownership, and ongoing tuning. |
Request a Demo
See how Proofpoint DSPM helps you discover sensitive data, prioritize exposure risk, and protect data across SaaS, cloud, and hybrid environments.
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Recommended for You
Choose a DSPM solution by evaluating coverage, classification depth, access governance, remediation, DLP integration, AI data security, and operational fit. The strongest option should help your team move from finding sensitive data to reducing exposure and preventing loss.
A leading enterprise DSPM solution should include sensitive data discovery, contextual classification, exposure risk analysis, over-permissioned access detection, remediation workflows, policy enforcement, compliance support, and integration with DLP and AI security tools.
DSPM matters for AI data security because AI tools can expose sensitive data through prompts, connected apps, RAG workflows, training and fine-tuning pipelines, and agent outputs. DSPM helps teams understand what data AI can access and reduce exposure before AI workflows are deployed.
DSPM coverage depends on the vendor, so buyers should verify support for Microsoft 365, SaaS apps, cloud platforms, databases, network-attached storage, and on-premises file stores. Broad coverage is important because sensitive data rarely stays in one environment.
DSPM supports secure AI adoption by identifying sensitive data that copilots, GenAI apps, model training and RAG pipelines, service accounts, and AI agents might access. It also helps teams reduce over-permissioned access and connect findings to enforcement workflows.
During a DSPM proof of concept, ask how the solution discovers data, classifies business-specific content, prioritizes exposure, remediates risky access, integrates with DLP, and supports AI data security. The goal is to validate how the platform performs in your real environment, not just in a demo.